title: Nonobstructive coronary artery disease as detected by 64-detector row cardiac computed tomographic angiography is associated with increased left ventricular mass.
abstract: BACKGROUND
  Cardiac computed tomographic angiography (CCTA) permits simultaneous assessment of coronary artery disease (CAD) and left ventricular mass (LVM). While increased LVM predicts mortality and is associated with obstructive CAD, the relationship of LVM with non-obstructive CAD is unknown.
  
  
  METHODS
  We evaluated 212 consecutive patients undergoing 64-detector row CCTA at 2 sites without evident cardiovascular disease or obstructive (≥70%) CAD by CCTA. LVM was measured by CCTA using Simpson's method of disks and indexed to body surface area (LVMI) and height to the allometric power of 2.7(LVM/ht2.7). CCTAs were evaluated by scoring a modified AHA 16-segment coronary artery model for none = 0 (0% stenosis), mild = 1 (1-49% stenosis) or moderate = 2 (50-69% stenosis). Overall CAD plaque burden was estimated by summing scores across all segments for a segment stenosis score (SSS, max = 32).
  
  
  RESULTS
  The mean age was 53.3 ± 12.8 with 52% female, 48% hypertensive, and 7.4% diabetic. The mean LVM was 109 ± 32.5 g; 58.5% had any coronary artery plaque. In multivariable linear regression, SSS was significantly associated with increased LVM, LVMI and LVM/ht2.7. LVM increased by 2.0 g for every 1-point increase in SSS (95% CI 0.06-3.4, p = 0.006). Agatston scores provided no additional predictive value for increased LVM above and beyond SSS.
  
  
  CONCLUSIONS
  Non-obstructive CAD visualized by CCTA is associated with increased LVM independent of effects of clinical risk factors and calcium scoring. Whether addition of LVM to stenosis assessment in patients undergoing CCTA enhances risk prediction of future CAD events warrants investigation.
authors.affiliation: Weill Cornell Medical College and New York Presbyterian Hospital, 520 E 70th Street, K415, New York, NY 10021, USA.
authors.forename: Fay Y
authors.initials: FY
authors.lastname: Lin
authors.forename: Danielle
authors.initials: D
authors.lastname: Nicolo
authors.forename: Richard B
authors.initials: RB
authors.lastname: Devereux
authors.forename: Troy M
authors.initials: TM
authors.lastname: Labounty
authors.forename: Allison
authors.initials: A
authors.lastname: Dunning
authors.forename: Millie
authors.initials: M
authors.lastname: Gomez
authors.forename: Sunaina
authors.initials: S
authors.lastname: Koduru
authors.forename: Jin-ho
authors.initials: JH
authors.lastname: Choi
authors.forename: Jonathan W
authors.initials: JW
authors.lastname: Weinsaft
authors.forename: Lauren A
authors.initials: LA
authors.lastname: Simprini
authors.forename: Tracy Q
authors.initials: TQ
authors.lastname: Callister
authors.forename: Leslee J
authors.initials: LJ
authors.lastname: Shaw
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
authors.forename: James K
authors.initials: JK
authors.lastname: Min
mesh_terms: D000328:Adult; D000368:Aged; D016009:Chi-Square Distribution; D017023:Coronary Angiography / Q000379:methods*; D023921:Coronary Stenosis / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D017379:Hypertrophy, Left Ventricular / Q000000981:diagnostic imaging*; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D009519:New York City; D011237:Predictive Value of Tests; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012720:Severity of Illness Index; D013714:Tennessee; D014057:Tomography, X-Ray Computed